Overview

Dataset statistics

Number of variables24
Number of observations3000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory562.6 KiB
Average record size in memory192.0 B

Variable types

Text1
Categorical14
Numeric8
DateTime1

Alerts

gross_margin_percentage has constant value ""Constant
branch is highly overall correlated with branch_name and 4 other fieldsHigh correlation
branch_name is highly overall correlated with branch and 4 other fieldsHigh correlation
city is highly overall correlated with branch and 4 other fieldsHigh correlation
city_manager_first_name is highly overall correlated with branch and 4 other fieldsHigh correlation
city_manager_last_name is highly overall correlated with branch and 4 other fieldsHigh correlation
cogs is highly overall correlated with gross_income and 4 other fieldsHigh correlation
gross_income is highly overall correlated with cogs and 4 other fieldsHigh correlation
manager_name is highly overall correlated with branch and 4 other fieldsHigh correlation
product_code is highly overall correlated with product_lineHigh correlation
product_line is highly overall correlated with product_codeHigh correlation
quantity is highly overall correlated with cogs and 3 other fieldsHigh correlation
tax_5% is highly overall correlated with cogs and 4 other fieldsHigh correlation
total is highly overall correlated with cogs and 4 other fieldsHigh correlation
unit_price is highly overall correlated with cogs and 3 other fieldsHigh correlation
year_of_txn is uniformly distributedUniform

Reproduction

Analysis started2024-03-21 09:54:54.699414
Analysis finished2024-03-21 09:55:09.104290
Duration14.4 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct1000
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:09.732246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters33000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row155-45-3814
2nd row810-60-6344
3rd row431-66-2305
4th row189-52-0236
5th row575-67-1508
ValueCountFrequency (%)
155-45-3814 3
 
0.1%
669-54-1719 3
 
0.1%
831-81-6575 3
 
0.1%
377-79-7592 3
 
0.1%
431-66-2305 3
 
0.1%
189-52-0236 3
 
0.1%
575-67-1508 3
 
0.1%
250-81-7186 3
 
0.1%
416-17-9926 3
 
0.1%
740-11-5257 3
 
0.1%
Other values (990) 2970
99.0%
2024-03-21T15:25:10.555698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 6000
18.2%
2 2871
8.7%
6 2862
8.7%
1 2850
8.6%
8 2832
8.6%
5 2781
8.4%
4 2754
8.3%
3 2727
8.3%
7 2685
8.1%
0 2427
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 6000
18.2%
2 2871
8.7%
6 2862
8.7%
1 2850
8.6%
8 2832
8.6%
5 2781
8.4%
4 2754
8.3%
3 2727
8.3%
7 2685
8.1%
0 2427
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 6000
18.2%
2 2871
8.7%
6 2862
8.7%
1 2850
8.6%
8 2832
8.6%
5 2781
8.4%
4 2754
8.3%
3 2727
8.3%
7 2685
8.1%
0 2427
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 6000
18.2%
2 2871
8.7%
6 2862
8.7%
1 2850
8.6%
8 2832
8.6%
5 2781
8.4%
4 2754
8.3%
3 2727
8.3%
7 2685
8.1%
0 2427
7.4%

branch
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
A
1020 
B
996 
C
984 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 1020
34.0%
B 996
33.2%
C 984
32.8%

Length

2024-03-21T15:25:10.770209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:11.088858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 1020
34.0%
b 996
33.2%
c 984
32.8%

Most occurring characters

ValueCountFrequency (%)
A 1020
34.0%
B 996
33.2%
C 984
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1020
34.0%
B 996
33.2%
C 984
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1020
34.0%
B 996
33.2%
C 984
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1020
34.0%
B 996
33.2%
C 984
32.8%

city
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Yangon
1011 
Mandalay
988 
Naypyitaw
978 
Detroit
 
5
Lakeland
 
4
Other values (4)
 
14

Length

Max length10
Median length9
Mean length7.654
Min length6

Characters and Unicode

Total characters22962
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNaypyitaw
2nd rowNaypyitaw
3rd rowMandalay
4th rowYangon
5th rowYangon

Common Values

ValueCountFrequency (%)
Yangon 1011
33.7%
Mandalay 988
32.9%
Naypyitaw 978
32.6%
Detroit 5
 
0.2%
Lakeland 4
 
0.1%
Fairfield 4
 
0.1%
Manhattan 4
 
0.1%
Chicago 3
 
0.1%
Metropolis 3
 
0.1%

Length

2024-03-21T15:25:11.285527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:11.442858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yangon 1011
33.7%
mandalay 988
32.9%
naypyitaw 978
32.6%
detroit 5
 
0.2%
lakeland 4
 
0.1%
fairfield 4
 
0.1%
manhattan 4
 
0.1%
chicago 3
 
0.1%
metropolis 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 5958
25.9%
n 3022
13.2%
y 2944
12.8%
o 1025
 
4.5%
g 1014
 
4.4%
Y 1011
 
4.4%
l 999
 
4.4%
t 999
 
4.4%
i 997
 
4.3%
d 996
 
4.3%
Other values (15) 3997
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5958
25.9%
n 3022
13.2%
y 2944
12.8%
o 1025
 
4.5%
g 1014
 
4.4%
Y 1011
 
4.4%
l 999
 
4.4%
t 999
 
4.4%
i 997
 
4.3%
d 996
 
4.3%
Other values (15) 3997
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5958
25.9%
n 3022
13.2%
y 2944
12.8%
o 1025
 
4.5%
g 1014
 
4.4%
Y 1011
 
4.4%
l 999
 
4.4%
t 999
 
4.4%
i 997
 
4.3%
d 996
 
4.3%
Other values (15) 3997
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5958
25.9%
n 3022
13.2%
y 2944
12.8%
o 1025
 
4.5%
g 1014
 
4.4%
Y 1011
 
4.4%
l 999
 
4.4%
t 999
 
4.4%
i 997
 
4.3%
d 996
 
4.3%
Other values (15) 3997
17.4%

customer_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Member
1503 
Normal
1497 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters18000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMember
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Member 1503
50.1%
Normal 1497
49.9%

Length

2024-03-21T15:25:11.613789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:11.759508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
member 1503
50.1%
normal 1497
49.9%

Most occurring characters

ValueCountFrequency (%)
e 3006
16.7%
m 3000
16.7%
r 3000
16.7%
M 1503
8.3%
b 1503
8.3%
N 1497
8.3%
o 1497
8.3%
a 1497
8.3%
l 1497
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3006
16.7%
m 3000
16.7%
r 3000
16.7%
M 1503
8.3%
b 1503
8.3%
N 1497
8.3%
o 1497
8.3%
a 1497
8.3%
l 1497
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3006
16.7%
m 3000
16.7%
r 3000
16.7%
M 1503
8.3%
b 1503
8.3%
N 1497
8.3%
o 1497
8.3%
a 1497
8.3%
l 1497
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3006
16.7%
m 3000
16.7%
r 3000
16.7%
M 1503
8.3%
b 1503
8.3%
N 1497
8.3%
o 1497
8.3%
a 1497
8.3%
l 1497
8.3%

gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Female
1503 
Male
1497 

Length

Max length6
Median length6
Mean length5.002
Min length4

Characters and Unicode

Total characters15006
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 1503
50.1%
Male 1497
49.9%

Length

2024-03-21T15:25:11.950755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:12.082502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 1503
50.1%
male 1497
49.9%

Most occurring characters

ValueCountFrequency (%)
e 4503
30.0%
a 3000
20.0%
l 3000
20.0%
F 1503
 
10.0%
m 1503
 
10.0%
M 1497
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4503
30.0%
a 3000
20.0%
l 3000
20.0%
F 1503
 
10.0%
m 1503
 
10.0%
M 1497
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4503
30.0%
a 3000
20.0%
l 3000
20.0%
F 1503
 
10.0%
m 1503
 
10.0%
M 1497
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4503
30.0%
a 3000
20.0%
l 3000
20.0%
F 1503
 
10.0%
m 1503
 
10.0%
M 1497
 
10.0%

product_line
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Fashion accessories
534 
Food and beverages
522 
Electronic accessories
510 
Sports and travel
498 
Home and lifestyle
480 

Length

Max length22
Median length19
Mean length18.54
Min length17

Characters and Unicode

Total characters55620
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectronic accessories
2nd rowElectronic accessories
3rd rowElectronic accessories
4th rowElectronic accessories
5th rowElectronic accessories

Common Values

ValueCountFrequency (%)
Fashion accessories 534
17.8%
Food and beverages 522
17.4%
Electronic accessories 510
17.0%
Sports and travel 498
16.6%
Home and lifestyle 480
16.0%
Health and beauty 456
15.2%

Length

2024-03-21T15:25:12.210957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:12.394752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
and 1956
24.6%
accessories 1044
13.1%
fashion 534
 
6.7%
food 522
 
6.6%
beverages 522
 
6.6%
electronic 510
 
6.4%
sports 498
 
6.3%
travel 498
 
6.3%
home 480
 
6.0%
lifestyle 480
 
6.0%
Other values (2) 912
11.5%

Most occurring characters

ValueCountFrequency (%)
e 7014
12.6%
a 5466
 
9.8%
s 5166
 
9.3%
4956
 
8.9%
o 4110
 
7.4%
c 3108
 
5.6%
r 3072
 
5.5%
n 3000
 
5.4%
t 2898
 
5.2%
i 2568
 
4.6%
Other values (15) 14262
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7014
12.6%
a 5466
 
9.8%
s 5166
 
9.3%
4956
 
8.9%
o 4110
 
7.4%
c 3108
 
5.6%
r 3072
 
5.5%
n 3000
 
5.4%
t 2898
 
5.2%
i 2568
 
4.6%
Other values (15) 14262
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7014
12.6%
a 5466
 
9.8%
s 5166
 
9.3%
4956
 
8.9%
o 4110
 
7.4%
c 3108
 
5.6%
r 3072
 
5.5%
n 3000
 
5.4%
t 2898
 
5.2%
i 2568
 
4.6%
Other values (15) 14262
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7014
12.6%
a 5466
 
9.8%
s 5166
 
9.3%
4956
 
8.9%
o 4110
 
7.4%
c 3108
 
5.6%
r 3072
 
5.5%
n 3000
 
5.4%
t 2898
 
5.2%
i 2568
 
4.6%
Other values (15) 14262
25.6%

unit_price
Real number (ℝ)

HIGH CORRELATION 

Distinct943
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.67213
Minimum10.08
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:12.580179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.08
5-th percentile15.279
Q132.875
median55.23
Q377.935
95-th percentile97.222
Maximum99.96
Range89.88
Interquartile range (IQR)45.06

Descriptive statistics

Standard deviation26.485792
Coefficient of variation (CV)0.47574599
Kurtosis-1.2185316
Mean55.67213
Median Absolute Deviation (MAD)22.505
Skewness0.0070703629
Sum167016.39
Variance701.4972
MonotonicityNot monotonic
2024-03-21T15:25:12.795616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.77 9
 
0.3%
48.63 6
 
0.2%
89.48 6
 
0.2%
84.05 6
 
0.2%
51.34 6
 
0.2%
34.42 6
 
0.2%
39.62 6
 
0.2%
87.87 6
 
0.2%
23.75 6
 
0.2%
73.47 6
 
0.2%
Other values (933) 2937
97.9%
ValueCountFrequency (%)
10.08 3
0.1%
10.13 3
0.1%
10.16 3
0.1%
10.17 3
0.1%
10.18 3
0.1%
10.53 3
0.1%
10.56 3
0.1%
10.59 3
0.1%
10.69 3
0.1%
10.75 3
0.1%
ValueCountFrequency (%)
99.96 6
0.2%
99.92 3
0.1%
99.89 3
0.1%
99.83 3
0.1%
99.82 6
0.2%
99.79 3
0.1%
99.78 3
0.1%
99.73 3
0.1%
99.71 3
0.1%
99.7 3
0.1%

quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.51
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:12.972677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9224556
Coefficient of variation (CV)0.53039122
Kurtosis-1.2154975
Mean5.51
Median Absolute Deviation (MAD)2
Skewness0.012928093
Sum16530
Variance8.5407469
MonotonicityNot monotonic
2024-03-21T15:25:13.121908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 357
11.9%
1 336
11.2%
4 327
10.9%
7 306
10.2%
5 306
10.2%
6 294
9.8%
9 276
9.2%
2 273
9.1%
3 270
9.0%
8 255
8.5%
ValueCountFrequency (%)
1 336
11.2%
2 273
9.1%
3 270
9.0%
4 327
10.9%
5 306
10.2%
6 294
9.8%
7 306
10.2%
8 255
8.5%
9 276
9.2%
10 357
11.9%
ValueCountFrequency (%)
10 357
11.9%
9 276
9.2%
8 255
8.5%
7 306
10.2%
6 294
9.8%
5 306
10.2%
4 327
10.9%
3 270
9.0%
2 273
9.1%
1 336
11.2%

tax_5%
Real number (ℝ)

HIGH CORRELATION 

Distinct990
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.379369
Minimum0.5085
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:13.286468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5085
5-th percentile1.955725
Q15.924875
median12.088
Q322.44525
95-th percentile39.1665
Maximum49.65
Range49.1415
Interquartile range (IQR)16.520375

Descriptive statistics

Standard deviation11.704921
Coefficient of variation (CV)0.76107938
Kurtosis-0.085613201
Mean15.379369
Median Absolute Deviation (MAD)7.50875
Skewness0.89167629
Sum46138.107
Variance137.00517
MonotonicityNot monotonic
2024-03-21T15:25:13.484817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.57 6
 
0.2%
39.48 6
 
0.2%
8.377 6
 
0.2%
13.188 6
 
0.2%
10.326 6
 
0.2%
10.3635 6
 
0.2%
22.428 6
 
0.2%
4.464 6
 
0.2%
4.154 6
 
0.2%
9.0045 6
 
0.2%
Other values (980) 2940
98.0%
ValueCountFrequency (%)
0.5085 3
0.1%
0.6045 3
0.1%
0.627 3
0.1%
0.639 3
0.1%
0.699 3
0.1%
0.767 3
0.1%
0.7715 3
0.1%
0.775 3
0.1%
0.814 3
0.1%
0.8875 3
0.1%
ValueCountFrequency (%)
49.65 3
0.1%
49.49 3
0.1%
49.26 3
0.1%
48.75 3
0.1%
48.69 3
0.1%
48.685 3
0.1%
48.605 3
0.1%
47.79 3
0.1%
47.72 3
0.1%
45.325 3
0.1%

total
Real number (ℝ)

HIGH CORRELATION 

Distinct990
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.96675
Minimum10.6785
Maximum1042.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:13.742582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.6785
5-th percentile41.070225
Q1124.42238
median253.848
Q3471.35025
95-th percentile822.4965
Maximum1042.65
Range1031.9715
Interquartile range (IQR)346.92787

Descriptive statistics

Standard deviation245.80333
Coefficient of variation (CV)0.76107938
Kurtosis-0.085613201
Mean322.96675
Median Absolute Deviation (MAD)157.68375
Skewness0.89167629
Sum968900.25
Variance60419.278
MonotonicityNot monotonic
2024-03-21T15:25:13.941959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263.97 6
 
0.2%
829.08 6
 
0.2%
175.917 6
 
0.2%
276.948 6
 
0.2%
216.846 6
 
0.2%
217.6335 6
 
0.2%
470.988 6
 
0.2%
93.744 6
 
0.2%
87.234 6
 
0.2%
189.0945 6
 
0.2%
Other values (980) 2940
98.0%
ValueCountFrequency (%)
10.6785 3
0.1%
12.6945 3
0.1%
13.167 3
0.1%
13.419 3
0.1%
14.679 3
0.1%
16.107 3
0.1%
16.2015 3
0.1%
16.275 3
0.1%
17.094 3
0.1%
18.6375 3
0.1%
ValueCountFrequency (%)
1042.65 3
0.1%
1039.29 3
0.1%
1034.46 3
0.1%
1023.75 3
0.1%
1022.49 3
0.1%
1022.385 3
0.1%
1020.705 3
0.1%
1003.59 3
0.1%
1002.12 3
0.1%
951.825 3
0.1%

time
Date

Distinct506
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Minimum2024-03-21 10:00:00
Maximum2024-03-21 20:59:00
2024-03-21T15:25:14.106389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:14.325610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payment
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Ewallet
1035 
Cash
1032 
Credit card
933 

Length

Max length11
Median length7
Mean length7.212
Min length4

Characters and Unicode

Total characters21636
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEwallet
2nd rowCredit card
3rd rowCredit card
4th rowCash
5th rowEwallet

Common Values

ValueCountFrequency (%)
Ewallet 1035
34.5%
Cash 1032
34.4%
Credit card 933
31.1%

Length

2024-03-21T15:25:14.575469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:14.705848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ewallet 1035
26.3%
cash 1032
26.2%
credit 933
23.7%
card 933
23.7%

Most occurring characters

ValueCountFrequency (%)
a 3000
13.9%
l 2070
9.6%
e 1968
9.1%
t 1968
9.1%
C 1965
9.1%
r 1866
8.6%
d 1866
8.6%
E 1035
 
4.8%
w 1035
 
4.8%
s 1032
 
4.8%
Other values (4) 3831
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21636
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3000
13.9%
l 2070
9.6%
e 1968
9.1%
t 1968
9.1%
C 1965
9.1%
r 1866
8.6%
d 1866
8.6%
E 1035
 
4.8%
w 1035
 
4.8%
s 1032
 
4.8%
Other values (4) 3831
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21636
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3000
13.9%
l 2070
9.6%
e 1968
9.1%
t 1968
9.1%
C 1965
9.1%
r 1866
8.6%
d 1866
8.6%
E 1035
 
4.8%
w 1035
 
4.8%
s 1032
 
4.8%
Other values (4) 3831
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21636
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3000
13.9%
l 2070
9.6%
e 1968
9.1%
t 1968
9.1%
C 1965
9.1%
r 1866
8.6%
d 1866
8.6%
E 1035
 
4.8%
w 1035
 
4.8%
s 1032
 
4.8%
Other values (4) 3831
17.7%

cogs
Real number (ℝ)

HIGH CORRELATION 

Distinct990
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.58738
Minimum10.17
Maximum993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:14.889587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.17
5-th percentile39.1145
Q1118.4975
median241.76
Q3448.905
95-th percentile783.33
Maximum993
Range982.83
Interquartile range (IQR)330.4075

Descriptive statistics

Standard deviation234.09841
Coefficient of variation (CV)0.76107938
Kurtosis-0.085613201
Mean307.58738
Median Absolute Deviation (MAD)150.175
Skewness0.89167629
Sum922762.14
Variance54802.066
MonotonicityNot monotonic
2024-03-21T15:25:15.088724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251.4 6
 
0.2%
789.6 6
 
0.2%
167.54 6
 
0.2%
263.76 6
 
0.2%
206.52 6
 
0.2%
207.27 6
 
0.2%
448.56 6
 
0.2%
89.28 6
 
0.2%
83.08 6
 
0.2%
180.09 6
 
0.2%
Other values (980) 2940
98.0%
ValueCountFrequency (%)
10.17 3
0.1%
12.09 3
0.1%
12.54 3
0.1%
12.78 3
0.1%
13.98 3
0.1%
15.34 3
0.1%
15.43 3
0.1%
15.5 3
0.1%
16.28 3
0.1%
17.75 3
0.1%
ValueCountFrequency (%)
993 3
0.1%
989.8 3
0.1%
985.2 3
0.1%
975 3
0.1%
973.8 3
0.1%
973.7 3
0.1%
972.1 3
0.1%
955.8 3
0.1%
954.4 3
0.1%
906.5 3
0.1%

gross_margin_percentage
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
4.761904762
3000 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters33000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.761904762
2nd row4.761904762
3rd row4.761904762
4th row4.761904762
5th row4.761904762

Common Values

ValueCountFrequency (%)
4.761904762 3000
100.0%

Length

2024-03-21T15:25:15.241204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:15.357967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.761904762 3000
100.0%

Most occurring characters

ValueCountFrequency (%)
4 6000
18.2%
7 6000
18.2%
6 6000
18.2%
. 3000
9.1%
1 3000
9.1%
9 3000
9.1%
0 3000
9.1%
2 3000
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 6000
18.2%
7 6000
18.2%
6 6000
18.2%
. 3000
9.1%
1 3000
9.1%
9 3000
9.1%
0 3000
9.1%
2 3000
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 6000
18.2%
7 6000
18.2%
6 6000
18.2%
. 3000
9.1%
1 3000
9.1%
9 3000
9.1%
0 3000
9.1%
2 3000
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 6000
18.2%
7 6000
18.2%
6 6000
18.2%
. 3000
9.1%
1 3000
9.1%
9 3000
9.1%
0 3000
9.1%
2 3000
9.1%

gross_income
Real number (ℝ)

HIGH CORRELATION 

Distinct990
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.379369
Minimum0.5085
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:15.489712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5085
5-th percentile1.955725
Q15.924875
median12.088
Q322.44525
95-th percentile39.1665
Maximum49.65
Range49.1415
Interquartile range (IQR)16.520375

Descriptive statistics

Standard deviation11.704921
Coefficient of variation (CV)0.76107938
Kurtosis-0.085613201
Mean15.379369
Median Absolute Deviation (MAD)7.50875
Skewness0.89167629
Sum46138.107
Variance137.00517
MonotonicityNot monotonic
2024-03-21T15:25:15.661173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.57 6
 
0.2%
39.48 6
 
0.2%
8.377 6
 
0.2%
13.188 6
 
0.2%
10.326 6
 
0.2%
10.3635 6
 
0.2%
22.428 6
 
0.2%
4.464 6
 
0.2%
4.154 6
 
0.2%
9.0045 6
 
0.2%
Other values (980) 2940
98.0%
ValueCountFrequency (%)
0.5085 3
0.1%
0.6045 3
0.1%
0.627 3
0.1%
0.639 3
0.1%
0.699 3
0.1%
0.767 3
0.1%
0.7715 3
0.1%
0.775 3
0.1%
0.814 3
0.1%
0.8875 3
0.1%
ValueCountFrequency (%)
49.65 3
0.1%
49.49 3
0.1%
49.26 3
0.1%
48.75 3
0.1%
48.69 3
0.1%
48.685 3
0.1%
48.605 3
0.1%
47.79 3
0.1%
47.72 3
0.1%
45.325 3
0.1%

rating
Real number (ℝ)

Distinct61
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9727
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:15.851199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.295
Q15.5
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7180071
Coefficient of variation (CV)0.24639052
Kurtosis-1.1517503
Mean6.9727
Median Absolute Deviation (MAD)1.5
Skewness0.0090006296
Sum20918.1
Variance2.9515486
MonotonicityNot monotonic
2024-03-21T15:25:16.090724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 78
 
2.6%
6.6 72
 
2.4%
4.2 66
 
2.2%
9.5 66
 
2.2%
6.5 63
 
2.1%
8 63
 
2.1%
5 63
 
2.1%
6.2 63
 
2.1%
5.1 63
 
2.1%
8.7 60
 
2.0%
Other values (51) 2343
78.1%
ValueCountFrequency (%)
4 33
1.1%
4.1 51
1.7%
4.2 66
2.2%
4.3 54
1.8%
4.4 51
1.7%
4.5 51
1.7%
4.6 24
 
0.8%
4.7 36
1.2%
4.8 39
1.3%
4.9 54
1.8%
ValueCountFrequency (%)
10 15
 
0.5%
9.9 48
1.6%
9.8 57
1.9%
9.7 42
1.4%
9.6 51
1.7%
9.5 66
2.2%
9.4 36
1.2%
9.3 48
1.6%
9.2 48
1.6%
9.1 42
1.4%

day_of_txn
Real number (ℝ)

Distinct31
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.256
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2024-03-21T15:25:16.247180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6906639
Coefficient of variation (CV)0.56965548
Kurtosis-1.2264564
Mean15.256
Median Absolute Deviation (MAD)8
Skewness0.049699381
Sum45768
Variance75.52764
MonotonicityNot monotonic
2024-03-21T15:25:16.403014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 132
 
4.4%
25 126
 
4.2%
19 123
 
4.1%
5 123
 
4.1%
8 123
 
4.1%
2 120
 
4.0%
14 117
 
3.9%
26 117
 
3.9%
7 114
 
3.8%
27 114
 
3.8%
Other values (21) 1791
59.7%
ValueCountFrequency (%)
1 84
2.8%
2 120
4.0%
3 108
3.6%
4 87
2.9%
5 123
4.1%
6 99
3.3%
7 114
3.8%
8 123
4.1%
9 111
3.7%
10 96
3.2%
ValueCountFrequency (%)
31 42
 
1.4%
30 60
2.0%
29 60
2.0%
28 90
3.0%
27 114
3.8%
26 117
3.9%
25 126
4.2%
24 99
3.3%
23 108
3.6%
22 84
2.8%

month_of_txn
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
1
1056 
3
1035 
2
909 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 1056
35.2%
3 1035
34.5%
2 909
30.3%

Length

2024-03-21T15:25:16.555426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:16.677152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1056
35.2%
3 1035
34.5%
2 909
30.3%

Most occurring characters

ValueCountFrequency (%)
1 1056
35.2%
3 1035
34.5%
2 909
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1056
35.2%
3 1035
34.5%
2 909
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1056
35.2%
3 1035
34.5%
2 909
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1056
35.2%
3 1035
34.5%
2 909
30.3%

year_of_txn
Categorical

UNIFORM 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
2021
1000 
2019
1000 
2020
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2019
3rd row2021
4th row2019
5th row2021

Common Values

ValueCountFrequency (%)
2021 1000
33.3%
2019 1000
33.3%
2020 1000
33.3%

Length

2024-03-21T15:25:16.810218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:16.943015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 1000
33.3%
2019 1000
33.3%
2020 1000
33.3%

Most occurring characters

ValueCountFrequency (%)
2 5000
41.7%
0 4000
33.3%
1 2000
 
16.7%
9 1000
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5000
41.7%
0 4000
33.3%
1 2000
 
16.7%
9 1000
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5000
41.7%
0 4000
33.3%
1 2000
 
16.7%
9 1000
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5000
41.7%
0 4000
33.3%
1 2000
 
16.7%
9 1000
 
8.3%

branch_name
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Canada
1020 
Chile
996 
US
984 

Length

Max length6
Median length5
Mean length4.356
Min length2

Characters and Unicode

Total characters13068
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowChile
4th rowCanada
5th rowCanada

Common Values

ValueCountFrequency (%)
Canada 1020
34.0%
Chile 996
33.2%
US 984
32.8%

Length

2024-03-21T15:25:17.120488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:17.303997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
canada 1020
34.0%
chile 996
33.2%
us 984
32.8%

Most occurring characters

ValueCountFrequency (%)
a 3060
23.4%
C 2016
15.4%
n 1020
 
7.8%
d 1020
 
7.8%
h 996
 
7.6%
i 996
 
7.6%
l 996
 
7.6%
e 996
 
7.6%
U 984
 
7.5%
S 984
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3060
23.4%
C 2016
15.4%
n 1020
 
7.8%
d 1020
 
7.8%
h 996
 
7.6%
i 996
 
7.6%
l 996
 
7.6%
e 996
 
7.6%
U 984
 
7.5%
S 984
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3060
23.4%
C 2016
15.4%
n 1020
 
7.8%
d 1020
 
7.8%
h 996
 
7.6%
i 996
 
7.6%
l 996
 
7.6%
e 996
 
7.6%
U 984
 
7.5%
S 984
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3060
23.4%
C 2016
15.4%
n 1020
 
7.8%
d 1020
 
7.8%
h 996
 
7.6%
i 996
 
7.6%
l 996
 
7.6%
e 996
 
7.6%
U 984
 
7.5%
S 984
 
7.5%

city_manager_first_name
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Jorge
1011 
Celeste
988 
Malcolm
978 
Lois
 
5
Frankie
 
4
Other values (4)
 
14

Length

Max length8
Median length7
Mean length6.6486667
Min length4

Characters and Unicode

Total characters19946
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalcolm
2nd rowMalcolm
3rd rowCeleste
4th rowJorge
5th rowJorge

Common Values

ValueCountFrequency (%)
Jorge 1011
33.7%
Celeste 988
32.9%
Malcolm 978
32.6%
Lois 5
 
0.2%
Frankie 4
 
0.1%
Kelvin 4
 
0.1%
Joseph 4
 
0.1%
Mark 3
 
0.1%
David 3
 
0.1%

Length

2024-03-21T15:25:17.459375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:17.613769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jorge 1011
33.7%
celeste 988
32.9%
malcolm 978
32.6%
lois 5
 
0.2%
frankie 4
 
0.1%
kelvin 4
 
0.1%
joseph 4
 
0.1%
mark 3
 
0.1%
david 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 3987
20.0%
l 2948
14.8%
o 1998
10.0%
r 1018
 
5.1%
J 1015
 
5.1%
g 1011
 
5.1%
1006
 
5.0%
s 997
 
5.0%
C 988
 
5.0%
t 988
 
5.0%
Other values (15) 3990
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3987
20.0%
l 2948
14.8%
o 1998
10.0%
r 1018
 
5.1%
J 1015
 
5.1%
g 1011
 
5.1%
1006
 
5.0%
s 997
 
5.0%
C 988
 
5.0%
t 988
 
5.0%
Other values (15) 3990
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3987
20.0%
l 2948
14.8%
o 1998
10.0%
r 1018
 
5.1%
J 1015
 
5.1%
g 1011
 
5.1%
1006
 
5.0%
s 997
 
5.0%
C 988
 
5.0%
t 988
 
5.0%
Other values (15) 3990
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3987
20.0%
l 2948
14.8%
o 1998
10.0%
r 1018
 
5.1%
J 1015
 
5.1%
g 1011
 
5.1%
1006
 
5.0%
s 997
 
5.0%
C 988
 
5.0%
t 988
 
5.0%
Other values (15) 3990
20.0%

city_manager_last_name
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Floyd
1011 
Webster
988 
Newman
978 
Jones
 
5
Murphie
 
4
Other values (4)
 
14

Length

Max length7
Median length6
Mean length5.9896667
Min length5

Characters and Unicode

Total characters17969
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNewman
2nd rowNewman
3rd rowWebster
4th rowFloyd
5th rowFloyd

Common Values

ValueCountFrequency (%)
Floyd 1011
33.7%
Webster 988
32.9%
Newman 978
32.6%
Jones 5
 
0.2%
Murphie 4
 
0.1%
Finch 4
 
0.1%
Almeco 4
 
0.1%
Brown 3
 
0.1%
Mallie 3
 
0.1%

Length

2024-03-21T15:25:17.794501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:17.969856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
floyd 1011
33.7%
webster 988
32.9%
newman 978
32.6%
jones 5
 
0.2%
murphie 4
 
0.1%
finch 4
 
0.1%
almeco 4
 
0.1%
brown 3
 
0.1%
mallie 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2970
16.5%
o 1023
 
5.7%
l 1021
 
5.7%
F 1015
 
5.6%
y 1011
 
5.6%
d 1011
 
5.6%
r 995
 
5.5%
s 993
 
5.5%
n 990
 
5.5%
W 988
 
5.5%
Other values (15) 5952
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2970
16.5%
o 1023
 
5.7%
l 1021
 
5.7%
F 1015
 
5.6%
y 1011
 
5.6%
d 1011
 
5.6%
r 995
 
5.5%
s 993
 
5.5%
n 990
 
5.5%
W 988
 
5.5%
Other values (15) 5952
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2970
16.5%
o 1023
 
5.7%
l 1021
 
5.7%
F 1015
 
5.6%
y 1011
 
5.6%
d 1011
 
5.6%
r 995
 
5.5%
s 993
 
5.5%
n 990
 
5.5%
W 988
 
5.5%
Other values (15) 5952
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2970
16.5%
o 1023
 
5.7%
l 1021
 
5.7%
F 1015
 
5.6%
y 1011
 
5.6%
d 1011
 
5.6%
r 995
 
5.5%
s 993
 
5.5%
n 990
 
5.5%
W 988
 
5.5%
Other values (15) 5952
33.1%

product_code
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
FA
534 
FB
522 
EA
510 
ST
498 
HL
480 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEA
2nd rowEA
3rd rowEA
4th rowEA
5th rowEA

Common Values

ValueCountFrequency (%)
FA 534
17.8%
FB 522
17.4%
EA 510
17.0%
ST 498
16.6%
HL 480
16.0%
HB 456
15.2%

Length

2024-03-21T15:25:18.207317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:18.367355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
fa 534
17.8%
fb 522
17.4%
ea 510
17.0%
st 498
16.6%
hl 480
16.0%
hb 456
15.2%

Most occurring characters

ValueCountFrequency (%)
F 1056
17.6%
A 1044
17.4%
B 978
16.3%
H 936
15.6%
E 510
8.5%
S 498
8.3%
T 498
8.3%
L 480
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 1056
17.6%
A 1044
17.4%
B 978
16.3%
H 936
15.6%
E 510
8.5%
S 498
8.3%
T 498
8.3%
L 480
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 1056
17.6%
A 1044
17.4%
B 978
16.3%
H 936
15.6%
E 510
8.5%
S 498
8.3%
T 498
8.3%
L 480
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 1056
17.6%
A 1044
17.4%
B 978
16.3%
H 936
15.6%
E 510
8.5%
S 498
8.3%
T 498
8.3%
L 480
8.0%

manager_name
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Jorge Floyd
1011 
Celeste Webster
988 
Malcolm Newman
978 
Lois Jones
 
5
Frankie Murphie
 
4
Other values (4)
 
14

Length

Max length16
Median length14
Mean length13.638333
Min length10

Characters and Unicode

Total characters40915
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalcolm Newman
2nd rowMalcolm Newman
3rd rowCeleste Webster
4th rowJorge Floyd
5th rowJorge Floyd

Common Values

ValueCountFrequency (%)
Jorge Floyd 1011
33.7%
Celeste Webster 988
32.9%
Malcolm Newman 978
32.6%
Lois Jones 5
 
0.2%
Frankie Murphie 4
 
0.1%
Kelvin Finch 4
 
0.1%
Joseph Almeco 4
 
0.1%
Mark Brown 3
 
0.1%
David Mallie 3
 
0.1%

Length

2024-03-21T15:25:18.561402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T15:25:18.738439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jorge 1011
16.9%
floyd 1011
16.9%
celeste 988
16.5%
webster 988
16.5%
malcolm 978
16.3%
newman 978
16.3%
lois 5
 
0.1%
jones 5
 
0.1%
finch 4
 
0.1%
almeco 4
 
0.1%
Other values (8) 28
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 6957
17.0%
4006
 
9.8%
l 3969
 
9.7%
o 3021
 
7.4%
r 2013
 
4.9%
s 1990
 
4.9%
t 1976
 
4.8%
a 1969
 
4.8%
m 1960
 
4.8%
J 1020
 
2.5%
Other values (23) 12034
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6957
17.0%
4006
 
9.8%
l 3969
 
9.7%
o 3021
 
7.4%
r 2013
 
4.9%
s 1990
 
4.9%
t 1976
 
4.8%
a 1969
 
4.8%
m 1960
 
4.8%
J 1020
 
2.5%
Other values (23) 12034
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6957
17.0%
4006
 
9.8%
l 3969
 
9.7%
o 3021
 
7.4%
r 2013
 
4.9%
s 1990
 
4.9%
t 1976
 
4.8%
a 1969
 
4.8%
m 1960
 
4.8%
J 1020
 
2.5%
Other values (23) 12034
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6957
17.0%
4006
 
9.8%
l 3969
 
9.7%
o 3021
 
7.4%
r 2013
 
4.9%
s 1990
 
4.9%
t 1976
 
4.8%
a 1969
 
4.8%
m 1960
 
4.8%
J 1020
 
2.5%
Other values (23) 12034
29.4%

Interactions

2024-03-21T15:25:07.116184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:58.297383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:59.836446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:01.127827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.327752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.479915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:04.715833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.938089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:07.249768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:58.462590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:59.990532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:01.274060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.474468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.614957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:04.867500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.059831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:07.382394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:58.843969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:00.117180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:01.410105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.628499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.786487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.037997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.241414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:07.611511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:58.987069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:00.241242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:01.569631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.771647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.975601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.194106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.373089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:07.741176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:59.167655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:00.398994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:01.690603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.907723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:04.102925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.324811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.518495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:07.899569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:59.342082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:00.570210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:01.885130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.038257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:04.218655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.461344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.663105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:08.023089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:59.502753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:00.730685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.059179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.174785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:04.372327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.578898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.821509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:08.173687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:24:59.668281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:00.941322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:02.211223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:03.325387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:04.544461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:05.757935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-21T15:25:06.954055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-21T15:25:19.104044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
branchbranch_namecitycity_manager_first_namecity_manager_last_namecogscustomer_typeday_of_txngendergross_incomemanager_namemonth_of_txnpaymentproduct_codeproduct_linequantityratingtax_5%totalunit_priceyear_of_txn
branch1.0001.0000.9960.9960.9960.0200.000-0.0130.0540.0200.9960.0330.0310.0640.0640.0160.0090.0200.0200.0290.000
branch_name1.0001.0000.9960.9960.9960.0200.000-0.0130.0540.0200.9960.0330.0310.0640.0640.0160.0090.0200.0200.0290.000
city0.9960.9961.0001.0001.000-0.0100.0210.0060.045-0.0101.0000.0430.0440.0360.036-0.0050.045-0.010-0.010-0.0170.066
city_manager_first_name0.9960.9961.0001.0001.0000.0170.021-0.0080.0450.0171.0000.0430.0440.0360.0360.0180.0600.0170.0170.0140.066
city_manager_last_name0.9960.9961.0001.0001.0000.0010.021-0.0040.0450.0011.0000.0430.0440.0360.036-0.006-0.0490.0010.0010.0150.066
cogs0.0200.020-0.0100.0170.0011.0000.0470.0020.0741.0000.0420.0550.0680.0560.0560.735-0.0171.0001.0000.6300.000
customer_type0.0000.0000.0210.0210.0210.0471.0000.0340.035-0.0170.0210.0420.0670.0410.041-0.0160.019-0.017-0.017-0.0190.000
day_of_txn-0.013-0.0130.006-0.008-0.0040.0020.0341.0000.0570.0020.0350.1480.0900.0830.083-0.043-0.0090.0020.0020.0570.000
gender0.0540.0540.0450.0450.0450.0740.0350.0571.000-0.0520.0450.0540.0480.0640.064-0.0740.004-0.052-0.0520.0160.000
gross_income0.0200.020-0.0100.0170.0011.000-0.0170.002-0.0521.0000.0420.0550.0680.0560.0560.735-0.0171.0001.0000.6300.000
manager_name0.9960.9961.0001.0001.0000.0420.0210.0350.0450.0421.0000.0430.0440.0360.0360.0180.0600.0170.0170.0140.066
month_of_txn0.0330.0330.0430.0430.0430.0550.0420.1480.0540.0550.0431.0000.0220.0690.069-0.015-0.042-0.023-0.023-0.0260.000
payment0.0310.0310.0440.0440.0440.0680.0670.0900.0480.0680.0440.0221.0000.0520.052-0.004-0.005-0.011-0.011-0.0150.000
product_code0.0640.0640.0360.0360.0360.0560.0410.0830.0640.0560.0360.0690.0521.0001.0000.020-0.0190.0360.0360.0190.000
product_line0.0640.0640.0360.0360.0360.0560.0410.0830.0640.0560.0360.0690.0521.0001.0000.020-0.0190.0360.0360.0190.000
quantity0.0160.016-0.0050.018-0.0060.735-0.016-0.043-0.0740.7350.018-0.015-0.0040.0200.0201.000-0.0150.7350.7350.0110.000
rating0.0090.0090.0450.060-0.049-0.0170.019-0.0090.004-0.0170.060-0.042-0.005-0.019-0.019-0.0151.000-0.017-0.017-0.0080.000
tax_5%0.0200.020-0.0100.0170.0011.000-0.0170.002-0.0521.0000.017-0.023-0.0110.0360.0360.735-0.0171.0001.0000.6300.000
total0.0200.020-0.0100.0170.0011.000-0.0170.002-0.0521.0000.017-0.023-0.0110.0360.0360.735-0.0171.0001.0000.6300.000
unit_price0.0290.029-0.0170.0140.0150.630-0.0190.0570.0160.6300.014-0.026-0.0150.0190.0190.011-0.0080.6300.6301.0000.000
year_of_txn0.0000.0000.0660.0660.0660.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-21T15:25:08.440588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T15:25:08.899848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

invoice_idbranchcitycustomer_typegenderproduct_lineunit_pricequantitytax_5%totaltimepaymentcogsgross_margin_percentagegross_incomeratingday_of_txnmonth_of_txnyear_of_txnbranch_namecity_manager_first_namecity_manager_last_nameproduct_codemanager_name
0155-45-3814CNaypyitawMemberFemaleElectronic accessories88.55835.4200743.820015:29:00Ewallet708.404.76190535.42004.71932021USMalcolmNewmanEAMalcolm Newman
1810-60-6344CNaypyitawNormalFemaleElectronic accessories40.86816.3440343.224014:38:00Credit card326.884.76190516.34406.5722019USMalcolmNewmanEAMalcolm Newman
2431-66-2305BMandalayNormalFemaleElectronic accessories88.25939.7125833.962520:51:00Credit card794.254.76190539.71257.61522021ChileCelesteWebsterEACeleste Webster
3189-52-0236AYangonNormalMaleElectronic accessories99.55734.8425731.692512:07:00Cash696.854.76190534.84257.61432019CanadaJorgeFloydEAJorge Floyd
4575-67-1508AYangonNormalMaleElectronic accessories38.6011.930040.530011:26:00Ewallet38.604.7619051.93006.72912021CanadaJorgeFloydEAJorge Floyd
5250-81-7186CNaypyitawNormalFemaleElectronic accessories99.6914.9845104.674510:23:00Credit card99.694.7619054.98458.02722019USMalcolmNewmanEAMalcolm Newman
6416-17-9926AYangonMemberFemaleElectronic accessories74.221037.1100779.310014:42:00Credit card742.204.76190537.11004.3112021CanadaJorgeFloydEAJorge Floyd
7740-11-5257CNaypyitawNormalMaleElectronic accessories24.741012.3700259.770016:44:00Cash247.404.76190512.37007.12422019USMalcolmNewmanEAMalcolm Newman
8794-32-2436BMandalayMemberMaleElectronic accessories55.6725.5670116.907015:08:00Ewallet111.344.7619055.56706.02732021ChileCelesteWebsterEACeleste Webster
9676-39-6028AYangonMemberFemaleElectronic accessories64.44516.1100338.310017:04:00Cash322.204.76190516.11006.63032021CanadaJorgeFloydEAJorge Floyd
invoice_idbranchcitycustomer_typegenderproduct_lineunit_pricequantitytax_5%totaltimepaymentcogsgross_margin_percentagegross_incomeratingday_of_txnmonth_of_txnyear_of_txnbranch_namecity_manager_first_namecity_manager_last_nameproduct_codemanager_name
2990343-75-9322BMandalayMemberFemaleSports and travel11.8584.740099.540016:34:00Cash94.804.7619054.74004.1912021ChileCelesteWebsterSTCeleste Webster
2991670-71-7306BMandalayNormalMaleSports and travel44.63613.3890281.169020:08:00Credit card267.784.76190513.38905.1212021ChileCelesteWebsterSTCeleste Webster
2992321-49-7382BMandalayMemberMaleSports and travel88.3114.415592.725517:38:00Credit card88.314.7619054.41555.21522020ChileCelesteWebsterSTCeleste Webster
2993361-59-0574BMandalayMemberMaleSports and travel90.53836.2120760.452014:48:00Credit card724.244.76190536.21206.51532021ChileCelesteWebsterSTCeleste Webster
2994311-13-6971BMandalayMemberMaleSports and travel31.991015.9950335.895015:18:00Credit card319.904.76190515.99509.92022020ChileCelesteWebsterSTCeleste Webster
2995361-59-0574BMandalayMemberMaleSports and travel90.53836.2120760.452014:48:00Credit card724.244.76190536.21206.51532020ChileCelesteWebsterSTCeleste Webster
2996311-13-6971BMandalayMemberMaleSports and travel31.991015.9950335.895015:18:00Credit card319.904.76190515.99509.92022021ChileCelesteWebsterSTCeleste Webster
2997670-71-7306BMandalayNormalMaleSports and travel44.63613.3890281.169020:08:00Credit card267.784.76190513.38905.1212020ChileCelesteWebsterSTCeleste Webster
2998253-12-6086AYangonMemberFemaleSports and travel98.40734.4400723.240012:43:00Credit card688.804.76190534.44008.71232020CanadaJorgeFloydSTJorge Floyd
2999343-75-9322BMandalayMemberFemaleSports and travel11.8584.740099.540016:34:00Cash94.804.7619054.74004.1912020ChileCelesteWebsterSTCeleste Webster